Global luxury retailer LVMH ($EPA:MC) plans to buy US jeweler Tiffany in a $16.2 billion deal that will further develop its position in the high-end fashion space. Tiffany ($TIF) shares shot up 6% in pre-market trading Monday morning November 25, on news of the deal. 

Our first map tracks all locations that would be part of the LVMH-Tiffany empire globally -  you can zoom in down to individual intersections to see Tiffany's locations and how close they are to LVMH stores.

Tiffany is like many other leading US retailers - because much of its inventory tends to move around the holiday (or, engagement) season, which is reflected in the chart above. Tiffany added 85% to job postings from 2019 lows, although this year's hiring peak doesn't match that of 2018. The Tiffany store count (not shown) has been steady for years, so the relative lack of growth in job posts is less concerning with additional data. 

The deal helps position LVMH in front of a valuable new segment of the luxury marketplace and to add a still-growing brand to a stable of products that could use a jolt. LVMH brand Sephora is losing ground to competing brands like Ulta ($ULTA), as the makeup space is rapidly diversifying and as social media disruptors are smashing down barriers to entry that for generations preserved the positions of legacy players in the space. 

Sephora has, according to Thinknum Alternative Data, 665 locations as part of a partnership with JC Penney ($JCP), but that also amounts to roughly three-quarters of the beleaguered retailer's US locations. 

But the Tiffany deal adds to LVMH portfolio a potent complement to other jewelry brands like TAG Heuer, Hublot and Bulgari - and billions in additional revenue, as well. 

And, we've also got a gif of the future LVMH-Tiffany footprint, which feels appropriate with the holiday season about to start. 

About the Data: 

Thinknum tracks companies using the information they post online - jobs, social and web traffic, product sales and app ratings - and creates data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales. 

Further Reading: 

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